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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
Condition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.
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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
Paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Foundations of Deep Reinforcement Learning: Theory and Practice in Python
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
Paperback. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 1.01.
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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Published by Addison-Wesley Professional (edition 1), 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
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Foundations of Deep Reinforcement Learning : Theory and Practice in Python
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
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Foundations of Deep Reinforcement Learning : Theory and Practice in Python
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Paperback or Softback)
Published by Addison-Wesley Professional 12/15/2019, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
Paperback or Softback. Condition: New. Foundations of Deep Reinforcement Learning: Theory and Practice in Python 1.1. Book.
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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
Condition: New.
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Foundations of Deep Reinforcement Learning
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Foundations of Deep Reinforcement Learning
Published by Pearson Education (US), US, 2020
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
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Add to basketPaperback. Condition: New. The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games-such as Go, Atari games, and DotA 2-to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Understand each key aspect of a deep RL problemExplore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)Understand how algorithms can be parallelized synchronously and asynchronouslyRun algorithms in SLM Lab and learn the practical implementation details for getting deep RL to workExplore algorithm benchmark results with tuned hyperparametersUnderstand how deep RL environments are designed Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
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Foundations of Deep Reinforcement Learning (Paperback)
Published by Pearson Education (US), Boston, 2020
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
Paperback. Condition: new. Paperback. Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games - such as Go, Atari games, and DotA 2 - to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelised synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designedThis guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Foundations of Deep Reinforcement Learning : Theory and Practice in Python
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
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Foundations of Deep Reinforcement Learning
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
Condition: New.
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Foundations of Deep Reinforcement Learning : Theory and Practice in Python
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Foundations of Deep Reinforcement Learning
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
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Foundations of Deep Reinforcement Learning: Theory and Practice in Python
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
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Add to basketPaperback. Condition: Brand New. 379 pages. 9.00x7.00x0.50 inches. In Stock.
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Foundations of Deep Reinforcement Learning (Paperback)
Published by Pearson Education (US), Boston, 2020
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
US$ 73.81
Convert currencyUS$ 50.52 shipping from United Kingdom to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: new. Paperback. Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games - such as Go, Atari games, and DotA 2 - to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelised synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designedThis guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Foundations of Deep Reinforcement Learning (Paperback)
Published by Pearson Education (US), Boston, 2020
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
US$ 91.85
Convert currencyUS$ 37.00 shipping from Australia to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: new. Paperback. Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games - such as Go, Atari games, and DotA 2 - to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelised synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designedThis guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Foundations of Deep Reinforcement Learning : Theory and Practice in Python
Published by Pearson Education Dez 2019, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
US$ 64.34
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Add to basketTaschenbuch. Condition: Neu. Neuware - In just a few years, deep reinforcement learning (DRL) systems such as DeepMinds DQN have yielded remarkable results. This hybrid approach to machine learning shares many similarities with human learning: its unsupervised self-learning, self-discovery of strategies, usage of memory, balance of exploration and exploitation, and its exceptional flexibility. Exciting in its own right, DRL may presage even more remarkable advances in general artificial intelligence.
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Foundations of Deep Reinforcement Learning
Published by Pearson Education (US), US, 2020
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
US$ 57.24
Convert currencyUS$ 88.75 shipping from United Kingdom to U.S.A.Quantity: 2 available
Add to basketPaperback. Condition: New. The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games-such as Go, Atari games, and DotA 2-to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Understand each key aspect of a deep RL problemExplore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)Understand how algorithms can be parallelized synchronously and asynchronouslyRun algorithms in SLM Lab and learn the practical implementation details for getting deep RL to workExplore algorithm benchmark results with tuned hyperparametersUnderstand how deep RL environments are designed Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
-
Foundations of Deep Reinforcement Learning: Theory and Practice in Python
Published by Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Language: English
US$ 63.56
Convert currencyUS$ 34.13 shipping from United Kingdom to U.S.A.Quantity: 2 available
Add to basketPaperback. Condition: Brand New. 379 pages. 9.00x7.00x0.50 inches. In Stock. This item is printed on demand.
